Estimating confidence regions over bounded domains

Bruno Eklund

No 548, SSE/EFI Working Paper Series in Economics and Finance from Stockholm School of Economics

Abstract: Estimating a density function over a bounded domain can be very complicated and resulting in an unsatisfactory or unrealistic density estimate. In many cases a one-to-one transformation can be applied to the considered data set, but there are also situations where such a unique transformation may not exist. This paper proposes a method to estimate confidence regions over bounded domains when a one-to-one transformation either does not exist or its existence is difficult to verify. By taking into account parameter restrictions of a underlying model, a nonlinear grid can be constructed, over which the density function can be estimated. The method is illustrated by applying it to the kurtosis/first-order autocorrelation of squared observations of the GARCH(1,1) model.

Keywords: Kernel estimation; nonlinear grid; GARCH model; highest density region (search for similar items in EconPapers)
JEL-codes: C14 C22 (search for similar items in EconPapers)
Pages: 12 pages
Date: 2003-11-28
New Economics Papers: this item is included in nep-ecm and nep-ets
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Published in Computational Statistics and Data Analysis, 2005, pages 349-360.

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Persistent link: https://EconPapers.repec.org/RePEc:hhs:hastef:0548

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